code: def __getitem__(self, index):
      if self.is_eval:
          sample = self.samples[index]
          sample_id = str(index)
      else:
          # select a random language
          lang = random.choice(list(self.samples.keys()))
          # select random sample
          index = random.randint(0, len(self.samples[lang]) - 1)
          sample = self.samples[lang][index]
          # a unique id for each sampel to deal with fails
          sample_id = lang + "_" + str(index)
  
      # ignore samples that we already know that is not valid ones
      if sample_id in self.failed_samples:
          if self.debug_failures:
              print(
                  f"Ignoring sample {sample['audio_file']} because it was already ignored before !!"
              )
          # call get item again to get other sample
          return self[1]
  
      # try to load the sample, if fails added it to the failed samples list
      try:
          (
              tseq,
              audiopath,
              wav,
              cond,
              cond_len,
              cond_idxs,
          ) = self.load_item(sample)
      except:
          if self.debug_failures:
              print(
                  f"error loading {sample['audio_file']} {sys.exc_info()}"
              )
          self.failed_samples.add(sample_id)
          return self[1]
  
      # check if the audio and text size limits and if it out of the limits, added it failed_samples
      if (
          wav is None
          or (
              self.max_wav_len is not None
              and wav.shape[-1] > self.max_wav_len
          )
          or (
              self.max_text_len is not None
              and tseq.shape[0] > self.max_text_len
          )
      ):
          # Basically, this audio file is nonexistent or too long to be supported by the dataset.
          # It's hard to handle this situation properly. Best bet is to return the a random valid token and skew the dataset somewhat as a result.
          if (
              self.debug_failures
              and wav is not None
              and tseq is not None
          ):
              print(
                  f"error loading {sample['audio_file']}: ranges are out of bounds; {wav.shape[-1]}, {tseq.shape[0]}"
              )
          self.failed_samples.add(sample_id)
          return self[1]
  
      res = {
          # 'real_text': text,
          "text": tseq,
          "text_lengths": torch.tensor(tseq.shape[0], dtype=torch.long),
          "wav": wav,
          "wav_lengths": torch.tensor(wav.shape[-1], dtype=torch.long),
          "filenames": audiopath,
          "conditioning": cond.unsqueeze(1),
          "cond_lens": torch.tensor(cond_len, dtype=torch.long)
          if cond_len is not torch.nan
          else torch.tensor([cond_len]),
          "cond_idxs": torch.tensor(cond_idxs)
          if cond_idxs is not torch.nan
          else torch.tensor([cond_idxs]),
      }
      return res
  
file_path: TTS/TTS/tts/layers/xtts/trainer/dataset.py
imports: import os
  import random
  import sys
  import torch
  import torch.nn.functional
  import torch.utils.data
  from TTS.tts.models.xtts import load_audio
label_desc: Use image node_id 5 for calling the XTTSDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: res, self, self, self